Graduate Thesis Or Dissertation
 

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/r494vs85w

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  • Manufacturing technology has continuously evolved and advanced over the past century; this has led to an increase in the production of consumer and industrial goods driven by simultaneous growth in population and wealth. Despite the resulting economic and labor growth, environmental impacts of manufacturing have increased dramatically due to the dependence on exhaustible material and energy resources necessary to meet these growing product demands. Increasing awareness and concern over these impacts has encouraged sustainable thinking toward managing material resources, alternative energy sources, and advanced manufacturing technologies. However, the primary emphasis of manufacturing system design decision making has remained focused on the reduction of cost of goods sold (in discrete part production) and total production cost (in continuous production). Manufacturing system design decision makers face challenges in defining, evaluating, and implementing sustainable manufacturing practices, which include the time-intensive nature of complex system design and analysis, data integrity, and deficiencies in assessment methods. In particular, the challenges of collecting, curating, analyzing, and presenting environmental, economic, and social metrics and indicators (sustainability performance information) remains a barrier to operational decision-making. Existing assessment methods and tools are not well-suited to evaluating the sustainability performance of manufacturing processes and systems, as they tend to be product-focused and have limited ability to adapt to changes at the manufacturing process or system level. The objective of this dissertation research is to facilitate sustainable manufacturing system design decision making by integrating a systematic and structured information modeling framework with a manufacturing system design approach. To accomplish this goal, the research approach involves four steps: (1) Performing a review of recent literature to identify the existing challenges in the development and application of sustainable manufacturing methods, tool, models, algorithms, metrics, and indicators; (2) Introducing a functional and object-oriented information modeling methodology to characterize the sustainability performance of unit manufacturing processes (UMPs) using the concepts of abstraction and instantiation, which is demonstrated by reusing and extending a manual milling UMP model for two and a half-axis milling process; (3) Applying information modeling approaches in characterizing the sustainability performance of manufacturing process flows composed of UMPs, which is demonstrated for a discrete part manufacturing system; and (4) Synthesizing the results of the prior steps to provide an information modeling framework for sustainable manufacturing system design decision making. The framework is applied to discrete and continuous product manufacturing to demonstrate the flexibility of this system design approach. The framework provides an accessible approach for detailed analysis of the sustainability performance of manufacturing processes and systems by enabling the reuse, extension, and composability of new and previously developed UMP models. The coupling of information modeling concepts (e.g., abstraction, instantiation, and polymorphism) along with hierarchical, structured, and systematic manufacturing system design enables the framework to address the challenges stated above, namely: (1) Modeling complexity is simplified through a bottom-up approach for characterizing individual UMPs, which are built up for system-level characterization; (2) Model development, verification, and validation efforts are reduced by reusing and extending UMP models, thereby also reducing the time-intensity of modeling; (3) Data reliability is improved, since the framework is agnostic of existing process-specific data sources, rather than restricting data sources and types necessary for analysis; and (4) Multi-criteria decision-making is facilitated by using a hierarchical data structure for model-quantified metrics of interest, which supports analysis using decision trees. The research lays a foundation for developing an ontologies based decision support for sustainable manufacturing system design, as ontologies describe relationships and links between systems and sub-systems which enables the framework to have high-fidelity and understanding of the manufacturing system model and data.
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